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<p>Facial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models. While psychological research suggests that expressions are often combinations of basic emotional units, most existing FER methods rely on deterministic point estimation, failing to model the intrinsic uncertainty and continuous nature of emotions. To address this, we propose POSTER-Var, a framework integrating a Variational Inference-based Classification Head (VICH). Unlike standard classifiers, VICH maps facial features into a probabilistic latent space via the reparameterization trick, enabling the model to learn the underlying distribution of expression intensities. Furthermore, we enhance feature representation by introducing layer embeddings and nonlinear transformations into the feature pyramid, facilitating the fusion of hierarchical semantic information. Extensive experiments on RAF-DB, AffectNet, and FER+ demonstrate that our method effectively handles fine-grained expression recognition, achieving state-of-the-art performance. The code has been open-sourced at: <a href="https://github.com/lg2578/poster-var">https://github.com/lg2578/poster-var</a>.</p>

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Facial expression recognition via variational inference

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Gang Lv,
  • JunLing Zhang,
  • Chiki Tsoi

摘要

Facial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models. While psychological research suggests that expressions are often combinations of basic emotional units, most existing FER methods rely on deterministic point estimation, failing to model the intrinsic uncertainty and continuous nature of emotions. To address this, we propose POSTER-Var, a framework integrating a Variational Inference-based Classification Head (VICH). Unlike standard classifiers, VICH maps facial features into a probabilistic latent space via the reparameterization trick, enabling the model to learn the underlying distribution of expression intensities. Furthermore, we enhance feature representation by introducing layer embeddings and nonlinear transformations into the feature pyramid, facilitating the fusion of hierarchical semantic information. Extensive experiments on RAF-DB, AffectNet, and FER+ demonstrate that our method effectively handles fine-grained expression recognition, achieving state-of-the-art performance. The code has been open-sourced at: https://github.com/lg2578/poster-var.